






'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

_UpperCamelCase						:						List[str]  =					logging.get_logger(__name__)

_UpperCamelCase						:						str  =					{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"imagegpt"
					a_        =					["past_key_values"]
					a_        =					{
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}




					def __init__(  self  :		List[str]				, _A  :		Union[str, Any]=5_12 + 1				, _A  :		List[str]=32 * 32				, _A  :		Tuple=5_12				, _A  :		Any=24				, _A  :		Dict=8				, _A  :		Any=None				, _A  :		str="quick_gelu"				, _A  :		Dict=0.1				, _A  :		Optional[int]=0.1				, _A  :		Any=0.1				, _A  :		Optional[Any]=1e-5				, _A  :		str=0.02				, _A  :		str=True				, _A  :		Optional[int]=True				, _A  :		Optional[Any]=False				, _A  :		List[str]=False				, _A  :		List[Any]=False				, **_A  :		Dict				, )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[Any]          =       vocab_size
								UpperCAmelCase_      :       List[str]          =       n_positions
								UpperCAmelCase_      :       str          =       n_embd
								UpperCAmelCase_      :       Tuple          =       n_layer
								UpperCAmelCase_      :       Optional[int]          =       n_head
								UpperCAmelCase_      :       str          =       n_inner
								UpperCAmelCase_      :       List[str]          =       activation_function
								UpperCAmelCase_      :       Tuple          =       resid_pdrop
								UpperCAmelCase_      :       int          =       embd_pdrop
								UpperCAmelCase_      :       Dict          =       attn_pdrop
								UpperCAmelCase_      :       List[Any]          =       layer_norm_epsilon
								UpperCAmelCase_      :       Tuple          =       initializer_range
								UpperCAmelCase_      :       Optional[Any]          =       scale_attn_weights
								UpperCAmelCase_      :       Optional[Any]          =       use_cache
								UpperCAmelCase_      :       Any          =       scale_attn_by_inverse_layer_idx
								UpperCAmelCase_      :       Optional[Any]          =       reorder_and_upcast_attn
								UpperCAmelCase_      :       Dict          =       tie_word_embeddings

								super().__init__(tie_word_embeddings=_A				, **_A      )






class  snake_case__      (					UpperCamelCase):




					@property
					def 		A      (  self  :		List[Any]      )    ->							Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
								    ]      )





					def 		A      (  self  :		int				, _A  :		"FeatureExtractionMixin"				, _A  :		int = 1				, _A  :		int = -1				, _A  :		bool = False				, _A  :		Optional["TensorType"] = None				, _A  :		int = 3				, _A  :		int = 32				, _A  :		int = 32				, )    ->							Mapping[str, Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       self._generate_dummy_images(_A				, _A				, _A				, _A      )
								UpperCAmelCase_      :       List[Any]          =       dict(preprocessor(images=_A				, return_tensors=_A      )      )

								return inputs










'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


